Relational databases are popular tools for organizing relational data for subsequent analysis. However, the data structures generally employed in relational databases are designed to access data infrequently, because this process consumes time and system resources and may create a “bottleneck” for system performance.
Additionally, relational databases may involve large amounts of data and data structures, and require complex coding to present database calls or otherwise query the database, as well as to communicate with user applications. Due to the complexity of the data and the required coding, a large investment of time and labor resources is necessary to create a functional relational database, as well as to validate the data and ensure the coding is operating properly.
Another consequence of this size and complexity is that relational databases and the associated application and coding may become unwieldy. This may create difficulties in moving relational databases between platforms, such as, for example, moving relational databases to platforms based on non-relational databases, or in performing further database or coding development and validation.
Accordingly, there is a need in the art for a faster and more efficient implementation of relational databases and for a cost-effective means to move existing relational databases between platforms.